Abstract 5706: ImmunoPROFILE: A prospective implementation of clinically validated, quantitative immune cell profiling test identifies tumor-infiltrating CD8+ and PD-1+ cell densities as prognostic biomarkers across a 2,023 patient pan-cancer cohort treated with different therapies
Authors: James Lindsay, Bijaya Sharma, Kristen D. Felt, Anita Giobbie-Hurder, Ian Dryg, Jason L. Weirather, Jennifer Altreuter, Tali Mazor, Priti Kumari, Joao V. Alessi, Ajit J. Nirmal, Michael P. Manos, Ananth R. Kumar, William Lotter, Ethan Cerami, Burce E. Johnson, Neil I. Lindeman, Lynette M. Sholl, Jonathan A. Nowak, Scott J. Rodig
Journal: Cancer Research (2023)

Abstract Tumor-infiltrating lymphocyte (TIL) density has been identified as a prognostic and predictive biomarker in select tumors treated with defined therapies. These observations suggest that TILs may be general markers of patient outcomes, but evidence in support of this hypothesis has been limited by small cohorts. We validated ImmunoPROFILE, a multiplexed immunofluorescence (MIF)-based assay coupled with machine-learning-based image analysis, to identify and quantify tumor cells (cytokeratin, PAX5, PAX8, SOX10), T cells (CD8), T-regulatory cells (FOXP3), exhausted cells (PD-1) and immunosuppressive tumor and immune cells (PD-L1). We applied the MIF panel to specimens from patients collected prospectively over three years and analyzed 2,023 cases across 27 tumor types. The association between biomarkers and overall survival (OS) was investigated using Cox models controlling for patient risk factors such as cancer type, metastatic vs. primary disease, age, and gender. Multivariable biomarker selection was based on likelihood ratios. The assay was highly robust (success rate 97%), reproducible (inter-scanning and intra-staining density controls within 1 SD, inter-staining PD-L1 scores ≤11% CV), and operator-independent (R2 &amp;gt;0.7 to &amp;gt;0.9 for each biomarker and 95% concordance in PD-L1 score-based interpretation between technicians). From whole slide images, a total of 11,932 individual regions of interest were analyzed across the cohort, resulting in &amp;gt;50 million spatially-resolved single cells which were summarized into cell population densities and PD-L1 scores. High densities of CD8+ (&amp;gt;64/mm2, p&amp;lt;0.0001), PD-1+ (&amp;gt;50/mm2, p&amp;lt;0.0001), and FOXP3+ (&amp;gt;30/mm2, p&amp;lt;0.0001) T cells were associated with longer overall survival (OS) irrespective of therapy and across all cancer types. PD-L1 metrics were not associated with OS (p=0.43). Compared to patients with low densities of CD8+ and PD-1+ cells, high densities of at least one of these cell types had better OS (Both high, HR: 0.49, 95% CI: 0.41 - 0.59; CD8+ high, HR: 0.63, (0.48 - 0.82); PD-1+ high, HR: 0.71, (0.54 - 0.93)). The results were consistent in the subset of patients (N=1572) who did not receive immunotherapy (IO). In patients who received IO therapy (N=451), only PD-1+ T-cell density associated with OS (HR: 0.48, (0.36 - 0.65)). To our knowledge, this is the first enterprise-level immune biomarker assay using multiplexed staining, digital imaging, and machine learning to be applied in a prospective manner to clinical specimens at scale. We found that select immune cell densities are prognostic across cancer types and therapies and demonstrated that quantification of multiple cell populations yields better prognostic power than single marker analyses. Citation Format: James Lindsay, Bijaya Sharma, Kristen D. Felt, Anita Giobbie-Hurder, Ian Dryg, Jason L. Weirather, Jennifer Altreuter, Tali Mazor, Priti Kumari, Joao V. Alessi, Ajit J. Nirmal, Michael P. Manos, Ananth R. Kumar, William Lotter, Ethan Cerami, Burce E. Johnson, Neil I. Lindeman, Lynette M. Sholl, Jonathan A. Nowak, Scott J. Rodig. ImmunoPROFILE: A prospective implementation of clinically validated, quantitative immune cell profiling test identifies tumor-infiltrating CD8+ and PD-1+ cell densities as prognostic biomarkers across a 2,023 patient pan-cancer cohort treated with different therapies. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5706.